NFL team efficiency rankings are back for 2008. The ratings are listed below in terms of generic win probability. The GWP is the probability a team would beat the league average team at a neutral site. Each team's opponent's average GWP is also listed, which can be considered to-date strength of schedule, and all ratings include adjustments for opponent strength.

Offensive rank (ORANK) is based on offensive generic win probability is based on each team's offensive efficiency stats only. In other words, it's the team's GWP assuming it had a league-average defense. DRANK is is a team's generic win probability rank assuming it had a league-average offense.GWP is based on a logistic regression model applied to current team stats. The model includes offensive and defensive passing and running efficiency, offensive turnover rates, and team penalty rates. A full explanation of the methodology can be found here. This year, however, I've made one important change based on research that strongly indicates that defensive interception rates are highly random and not consistent throughout the year. Accordingly, I've removed them from the model and updated the weights of the remaining stats.

RANK

TEAM

LAST WK

GWP

Opp GWP

ORANK

DRANK

1

WAS

1

0.86

0.70

1

4

2

NYG

3

0.84

0.42

2

3

3

SD

2

0.79

0.64

4

13

4

PHI

5

0.76

0.61

6

7

5

CAR

4

0.75

0.54

5

1

6

MIA

18

0.74

0.61

8

20

7

DAL

6

0.70

0.49

3

11

8

ARI

8

0.68

0.64

10

17

9

CHI

12

0.67

0.52

9

2

10

NYJ

14

0.64

0.64

20

10

11

DEN

15

0.59

0.50

7

28

12

BUF

9

0.58

0.41

23

14

13

NO

7

0.58

0.58

13

15

14

PIT

13

0.57

0.42

17

5

15

MIN

10

0.56

0.55

14

12

16

OAK

16

0.52

0.54

22

16

17

TB

19

0.52

0.55

16

9

18

TEN

11

0.51

0.40

24

8

19

ATL

20

0.50

0.39

11

24

20

IND

21

0.47

0.49

12

22

21

BAL

23

0.44

0.39

25

6

22

NE

27

0.44

0.49

27

23

23

GB

26

0.42

0.48

18

19

24

SF

17

0.41

0.41

30

18

25

JAX

22

0.39

0.52

15

27

26

HOU

25

0.38

0.50

19

26

27

SEA

24

0.33

0.53

26

31

28

CIN

29

0.29

0.54

28

21

29

STL

28

0.27

0.63

21

30

30

KC

30

0.20

0.57

32

29

31

CLE

31

0.16

0.50

29

25

32

DET

32

0.13

0.51

31

32

To give everyone an insight into why the rankings are what they are, here are the team efficiency stats.

11 Responses to “Week 5 Efficiency Rankings”

Anytime the Redskins are at the top of a set of rankings, I should just leave well enough alone, but I'm curious as to how much a few turnover will hurt them. With them having fumble and interception rates at zero, even with regression to the mean factored in, they are still leaps and bounds ahead of the pack. Do you see a big drop after they pick up a few of each?

Also, I'm not sure if you ever click my link, but I put up soccer probabilities last week and I realized I had no idea how to grade the results. I'm not a big time stats whiz so I was wondering if you had any thoughts on how to determine the quality of the picks.

Josh-The Redskins have played very well against the most difficult schedule by far. They'll eventually have some turnovers and lose some games, but if you're a fan there's every reason to be optimistic. Their problem is their division.

For grading probabilities, one method is to use squared errors. For example, if you predict a .90/.10 game and get it wrong, that's a .9-0=.9 error. If you predict a .6/.4 game and get it right, that's a 1-.6=.4 error. You square all the errors then add them up, then take the square root. There's no "good" or "bad" error number. It's all relative depending on how predictable your subject is. It's useful for comparing various models. The idea is not only to identify the favorite, but to estimate the confidence level in the model.

I guess I should have been a bit clearer. My problem with the soccer matches is the fact that there are ties. Since the probabilities are something like 40/35/25, is it still the same situation? I'm guessing it just makes the error higher, which isn't necessarily bad.

Question on FUMRATE. I noticed that Washington has a FUMRATE of 0. In week 2, however, Antwaan Randle-El fumbled a punt. Does this efficiency stat only account for fumbles that occur on offense and not on special teams?

I was curious about the following statement: "The GWP is the probability a team would beat the league average team at a neutral site." Is there a way for your audience to tweek these numbers if a team is playing a little bit better of a team then average? Not necessarily a good team but a team that's just better then the average

I absolutely love this site. Your work brings a bit of science to a field that feels like it's filled with carnival con men.

Your site has caused me to start diving into learning more about regressions for the first time since my stats classes back at school. Would you have any suggestions on software to use for working on this type of problem. I've started playing around with R and elrm but was interested to see if there was anything else you would suggest.

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